Genetics Core

遗传学核心

• 批准号: 10495157
• 负责人: ANDREW J SAYKIN
• 金额: $ 96.35万
• 依托单位: NORTHERN CALIFORNIA INSTITUTE/RES/EDU
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10495157
• 关键词: Address; Adverse effects; Age of Onset; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease related dementia; Amyloid; Artificial Intelligence; Asian; Autophagocytosis; Bioinformatics; Biological; Biological Assay; Biological Markers; Biological Process; Black race; Blood Vessels; Candidate Disease Gene; Clinical; Clinical Trials; Clinical Trials Design; Cognition; Cognitive; Collaborations; Collection; Communities; Complex; DNA; DNA methylation profiling; Data; Development; Diagnostic; Disease; Endocytosis; Enrollment; Ensure; Fostering; Foundations; Funding; Future; Generations; Genes; Genetic; Genetic Markers; Genetic Models; Genetic Transcription; Genomics; Genotype; Goals; Grant; Heritability; Immune; Individual; Industry; Investigation; Knowledge; Koreans; Latinx; Machine Learning; Measures; Methods; Modeling; Molecular; Multimodal Imaging; Nerve Degeneration; Outcome; Pathology; Pathway interactions; Peripheral Blood Mononuclear Cell; Phase; Play; Predisposition; Quality Control; RNA; Reproducibility; Resources; Risk; Risk Assessment; Role; Sample Size; Sampling; Science; Stem Cell Development; Stratification; Systems Biology; Therapeutic Agents; Underrepresented Populations; Validation; biobank; cohort; cost; deep learning; drug development; drug discovery; endophenotype; genetic analysis; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic locus; health disparity; improved; induced pluripotent stem cell; industry partner; knowledge base; lipid metabolism; lipidomics; low socioeconomic status; machine learning model; metabolomics; multi-ethnic; multiple omics; neuroimaging; new therapeutic target; phenotypic biomarker; polygenic risk score; power analysis; pre-clinical; precision medicine; predictive marker; programs; recruit; repository; sample collection; screening; synergism; tau Proteins; trial design; whole genome; working group

Genetics Core: Summary/Abstract The overarching goal of the ADNI Genetics Core is to support the identification and validation of genetic markers for use in clinical trials and drug discovery, in alignment with Project 1 and the broader NIA and NAPA goals of effective precision medicine for AD/ADRD. AD is a complex and highly heritable disease, with genetics playing an important role in identifying novel targets for therapeutics, diagnostics, and risk assessment. APOE remains an attractive target and is widely employed for stratification and enrichment of clinical trials, as it influences onset age, amyloid accumulation, and susceptibility to adverse effects of anti-amyloid treatment. Large scale genome- wide association studies (GWAS), most including ADNI data, have identified multiple genes and pathways related to amyloid and tau, immune dysregulation, lipid metabolism, vascular integrity, endocytosis/autophagy, and other biological mechanisms. The core will continue its focus on collaboratively advancing genetics and related omics to discover, validate, and implement markers that can improve the precision and power of AD clinical trials. Specific Aims for ADNI4: (1) Continue longitudinal sample collection, processing, banking, curation, and dissemination in partnership with NCRAD, including baseline PBMC collection to support future iPSCs development or other NIA-approved uses by the scientific community; (2) Continue providing genome-wide genotyping data, including APOE, with new emphasis on diverse ancestry; (3) Support the ADNI4 multiethnic screening strategy (n=4000); (4) Continue to perform and facilitate bioinformatic analyses of genetic and quantitative endophenotype data related to the goals of ADNI, which include improvement of clinical trial design through enrichment using genetic markers. With Project 1, we will assess genetic models including APOE and polygenic risk scores (PRS) as predictors of biomarker and cognitive outcomes. The Core will continue to explore the benefit of adding transcriptional risk scores (TRS) and DNA methylation profiling in candidate gene loci to models. Using a systems biology framework, genetic data will be used to predict trajectories of amyloid, tau, neurodegeneration, and vascular pathways (A/T/N/V) as measured by ADNI biomarkers, as well as clinical course. The Core will also continue to explore how advanced AI methods (machine learning and deep learning) can contribute to AD genetic analyses, working with affiliated NIA programs including ADSP, AMP-AD and AI4AD. With the AD Metabolomics Consortium, we will continue to assess genes related to longitudinal metabolomics/lipidomics changes in AD; and (5) Continue as a “hub” providing organization, collaboration, and support for genomic studies of quantitative biomarker phenotypes and outcomes in ADNI, including fostering new knowledge through team science working groups. Funded or pending collaborations will support sequencing of previously collected ADNI DNA and RNA samples, the application of AI and systems biology, and analysis of diverse cohorts, adding new collaborations with Asian AD consortia including the ADNI-like KBASE2 cohort.

遗传学核心：总结/摘要 ADNI 遗传学核心的总体目标是支持遗传标记的识别和验证 用于临床试验和药物发现，与项目 1 以及更广泛的 NIA 和 NAPA 目标保持一致 AD/ADRD 的有效精准治疗 AD 是一种复杂且高度遗传的疾病，与遗传因素有关。 在确定治疗、诊断和风险评估的新靶点方面仍然发挥着重要作用。 一个有吸引力的目标，广泛应用于临床试验的分层和丰富化，因为它影响起效 年龄、淀粉样蛋白积累和抗淀粉样蛋白治疗不良反应的易感性。 广泛关联研究 (GWAS)，大部分包括 ADNI 数据，已经确定了多个基因和通路 与淀粉样蛋白和 tau 蛋白、免疫失调、脂质代谢、血管完整性、内吞作用/自噬相关， 和其他生物机制。核心将继续致力于合作推进遗传学和 相关组学来发现、验证和实施可以提高 AD 精度和功效的标记 ADNI4 的具体目标： (1) 继续纵向样本收集、处理、存储、管理， 与 NCRAD 合作传播，包括收集基线 PBMC 以支持未来的 iPSC (2) 继续提供全基因组 基因分型数据，包括 APOE，新强调多样化血统；(3) 支持 ADNI4 多种族； 筛选策略（n = 4000）；（4）继续进行和促进遗传和生物信息学分析 与 ADNI 目标相关的内表型数据，包括临床试验设计的定量改进 通过使用遗传标记进行富集，我们将评估包括 APOE 和 The Core 将继续探索多基因风险评分 (PRS) 作为生物标志物和认知结果的预测因子。 在候选基因位点中添加转录风险评分 (TRS) 和 DNA 甲基化分析的好处 使用系统生物学框架，遗传数据将用于预测淀粉样蛋白、tau 蛋白的轨迹， 通过 ADNI 生物标志物测量的神经退行性变和血管通路 (A/T/N/V) 以及临床 The Core还将继续探索先进的AI方法（机器学习和深度学习）。 可以与附属的 NIA 项目（包括 ADSP、AMP-AD 和 AI4AD。我们将与 AD 代谢组学联盟一起继续评估与纵向相关的基因。 AD 中代谢组学/脂质组学的变化；以及 (5) 继续作为提供组织、协作和服务的“中心”； 支持 ADNI 中定量生物标志物表型和结果的基因组研究，包括培育 通过团队科学工作组获得的新知识或待定的合作将支持测序。 之前收集的 ADNI DNA 和 RNA 样本、人工智能和系统生物学的应用以及分析 多样化的队列，增加了与亚洲 AD 联盟的新合作，包括类似 ADNI 的 KBASE2 队列。